Analysis of the Quantization Noise in Discrete Wavelet Transform Filters for Image Processing

Chervyakov, Nikolay I.; Lyakhov, Pavel; Kaplun, Dmitry; Butusov, Denis N.; Nagornov, Nikolay

doi:10.3390/electronics7080135

Cited by 33 publications

(23 citation statements)

References 29 publications

(32 reference statements)

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“…Authors [33] quantized wavelet filters coefficients by 16 bits, but there is no rationale for this choice. The problem of analyzing the quantization noise effect in wavelet filters coefficients for 2D grayscale and color images DWT with 8 bits per color (BPC) was solved in [43].…”

Section: Introductionmentioning

confidence: 99%

Analysis of the Quantization Noise in Discrete Wavelet Transform Filters for 3D Medical Imaging

2020

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Denoising and compression of 2D and 3D images are important problems in modern medical imaging systems. Discrete wavelet transform (DWT) is used to solve them in practice. We analyze the quantization noise effect in coefficients of DWT filters for 3D medical imaging in this paper. The method for wavelet filters coefficients quantizing is proposed, which allows minimizing resources in hardware implementation by simplifying rounding operations. We develop the method for estimating the maximum error of 3D grayscale and color images DWT with various bits per color (BPC). The dependence of the peak signal-to-noise ratio (PSNR) of the images processing result on wavelet used, the effective bit-width of filters coefficients and BPC is revealed. We derive formulas for determining the minimum bit-width of wavelet filters coefficients that provide a high (PSNR ≥ 40 dB for images with 8 BPC, for example) and maximum (PSNR = ∞ dB) quality of 3D medical imaging by DWT depending on wavelet used. The experiments of 3D tomographic images processing confirmed the accuracy of theoretical analysis. All data are presented in the fixed-point format in the proposed method of 3D medical images DWT. It is making possible efficient, from the point of view of hardware and time resources, the implementation for image denoising and compression on modern devices such as field-programmable gate arrays and application-specific integrated circuits.

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Section: Introductionmentioning

confidence: 99%

Analysis of the Quantization Noise in Discrete Wavelet Transform Filters for 3D Medical Imaging

2020

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“…The electromagnetic disturbances can have characteristics of processed information [18,19]. Such disturbances are called "valuable (sensitive) emissions".…”

Section: Valuable Emissionsmentioning

confidence: 99%

Impact of IT Devices Production Quality on the Level of Protection of Processed Information against the Electromagnetic Infiltration Process

Kubiak

2019

Electronics

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Due to the variety and multiplicity of electronic devices, the issue of electromagnetic environment protection is becoming more and more important. We often hear about how necessary it is for electronic devices to meet appropriate requirements. Meeting these requirements determines whether a device can be marketed. Unfortunately, the electrical parameters of electronic components have a very wide range of tolerances. For this reason, measured values of electromagnetic disturbances generated by devices of the same type are not always identical. Differences between those values may reach up to several dB. This problem also concerns electromagnetic emissions correlated with the processed information, which are very sensitive to electromagnetic infiltration process. Issues related to the problem of protection of electromagnetic environment are shown on the basis of research results obtained for several devices of the same type. Mentioned level differences of electromagnetic emissions can decide about a classification of device from viewpoint of protection of information against electromagnetic penetration process. These differences may be a treat to information security. Higher levels of valuable emissions force an application of additional methods limiting an effectiveness of electromagnetic penetration process. This particularly applies to IT devices with a wide range of applications, e.g., laptops and desktop computers. In this paper, this phenomenon was presented on the basis of tests of several devices of the same type. Also, there were carried out analyses of influence of increase levels of valuable emissions on a security zone radius.

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“…In the field of electronics and information, signal processing is a hot research topic, and as a special signal, the study of image has attracted the attention of scholars all over the world [1][2][3]. In image processing, image restoration is one of the most important issues and this issue has received extensive attention in the past few decades [4][5][6][7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

A Novel Image-Restoration Method Based on High-Order Total Variation Regularization Term

Xiang

Wang

et al. 2019

Electronics

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This paper presents two new models for solving image the deblurring problem in the presence of impulse noise. One involves a high-order total variation (TV) regularizer term in the corrected total variation L1 (CTVL1) model and is named high-order corrected TVL1 (HOCTVL1). This new model can not only suppress the defects of the staircase effect, but also improve the quality of image restoration. In most cases, the regularization parameter in the model is a fixed value, which may influence processing results. Aiming at this problem, the spatially adapted regularization parameter selection scheme is involved in HOCTVL1 model, and spatially adapted HOCTVL1 (SAHOCTVL1) model is proposed. When dealing with corrupted images, the regularization parameter in SAHOCTVL1 model can be updated automatically. Many numerical experiments are conducted in this paper and the results show that the two models can significantly improve the effects both in visual quality and signal-to-noise ratio (SNR) at the expense of a small increase in computational time. Compared to HOCTVL1 model, SAHOCTVL1 model can restore more texture details, though it may take more time.

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Analysis of the Quantization Noise in Discrete Wavelet Transform Filters for Image Processing

Cited by 33 publications

References 29 publications

Analysis of the Quantization Noise in Discrete Wavelet Transform Filters for 3D Medical Imaging

Analysis of the Quantization Noise in Discrete Wavelet Transform Filters for 3D Medical Imaging

Impact of IT Devices Production Quality on the Level of Protection of Processed Information against the Electromagnetic Infiltration Process

A Novel Image-Restoration Method Based on High-Order Total Variation Regularization Term

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